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Increasing evidence suggests that physical activity could delay or attenuate the symptoms of Alzheimer's disease (AD). But the underlying mechanisms are still not fully understood. To investigate the effect of long-term treadmill exercise on the spatial memory of AD mice and the possible role of β-amyloid, brain-derived neurotrophic factor (BDNF) and microglia in the effect, male APPswe/PS1dE9 AD mice aged 4 months were subjected to treadmill exercise for 5 months with 6 sessions per week and gradually increased load. A Morris water maze was used to evaluate the spatial memory. Expression levels of β-amyloid, BDNF and Iba-1 (a microglia marker) in brain tissue were detected by immunohistochemistry. Sedentary AD mice and wildtype C57BL/6J mice served as controls. The results showed that 5-month treadmill exercise significantly decreased the escape latencies (P < 0.01 on the 4th day) and improved the spatial memory of the AD mice in the water maze test. Meanwhile, treadmill exercise significantly increased the number of BDNF-positive cells and decreased the ratios of activated microglia in both the cerebral cortex and the hippocampus. However, treadmill exercise did not significantly alleviate the accumulation of β-amyloid in either the cerebral cortex or the hippocampus of the AD mice (P > 0.05). The study suggested that long-term treadmill exercise could improve the spatial memory of the male APPswe/PS1dE9 AD mice. The increase in BDNF-positive cells and decrease in activated microglia might underpin the beneficial effect.

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Although successful virologic outcomes were seen in the vast majority (75.3%) of those treated at one year, virologic failure continues to be a problem particularly among those less adherent and from Xinjiang. Additional data are needed to understand the generalizability of these results, particularly those related to Xinjiang. For IDUs, enhancing adherence to HAART and considering the treatment of drug addiction as an integral part of the treatment for HIV infection should be considered. As China's National Free Antiretroviral Treament Program continues to mature and improve, ramping up treatment in these settings may be important considerations to the long-term success of the program.

In linear regression models with high dimensional data, the classical z-test (or t-test) for testing the significance of each single regression coefficient is no longer applicable. This is mainly because the number of covariates exceeds the sample size. In this paper, we propose a simple and novel alternative by introducing the Correlated Predictors Screening (CPS) method to control for predictors that are highly correlated with the target covariate. Accordingly, the classical ordinary least squares approach can be employed to estimate the regression coefficient associated with the target covariate. In addition, we demonstrate that the resulting estimator is consistent and asymptotically normal even if the random errors are heteroscedastic. This enables us to apply the z-test to assess the significance of each covariate. Based on the p-value obtained from testing the significance of each covariate, we further conduct multiple hypothesis testing by controlling the false discovery rate at the nominal level. Then, we show that the multiple hypothesis testing achieves consistent model selection. Simulation studies and empirical examples are presented to illustrate the finite sample performance and the usefulness of the proposed method, respectively.

The pathogenesis of small cell lung cancer (SCLC), a highly metastatic malignant tumor, remains unclear. In the present study, important genes and pathways that are involved in the pathogenesis of SCLC were identified. The following four datasets were downloaded from the Gene Expression Omnibus: GSE60052, GSE43346, GSE15240 and GSE6044. The differentially expressed genes (DEGs) between the SCLC samples and the normal samples were analyzed using R software. The limma package was used for every dataset. The RobustRankAggreg package was used to integrate the DEGs from the four datasets. Functional and pathway enrichment analyses were conducted using the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases with FunRich software and R software, respectively. In addition, the protein-protein interaction (PPI) network of the DEGs was constructed using the STRING database and Cytoscape software. Hub genes and significant modules were identified using Molecular Complex Detection in Cytoscape software. Finally, the expression values of hub genes were determined using the Oncomine online database. In total, 412 DEGs were identified following the integration of the four datasets, with 146 upregulated genes and 266 downregulated genes. The upregulated DEGs were primarily enriched in the cell cycle, cell division and microtubule binding. The downregulated DEGs were primarily enriched in the complement and coagulation cascades, the cytokine-mediated signaling pathway and protein binding. Eight hub genes and 1 significant module correlated to the cell cycle pathway were identified based on a subset of the PPI network. Finally, five hub genes were identified as highly expressed in SCLC tissue compared with normal tissue. The cell cycle pathway may be the pathway most closely associated with the pathogenesis of SCLC. NDC80, BUB1B, PLK1, CDC20 and MAD2L1 should be the focus of follow-up studies regarding the diagnosis and treatment of SCLC.